The ongoing climate change and increasingly strict climate goals of the European Union demand decisive action in all sectors. Especially in manufacturing industry, demand response measures have a high potential to balance the industrial electricity consumption with the increasingly volatile electricity supply from renewable sources. This work aims to develop a method to forecast the electrical energy demand of metal cutting machine tools as a necessary input for implementing demand response measures in factories. Building on the results of a previous study, long short-term memory networks (LSTM) and convolutional neural networks (CNN) are examined in their performance for forecasting the electric load of a machine tool for a 100 second time horizon. The results show that especially the combination of CNN and LSTM in a deep learning approach generates accurate and robust time series forecasts with reduced feature preparation effort. To further improve the forecasting accuracy, different network architectures including an attention mechanism for the LSTMs and different hyperparameter combinations are evaluated. The results are validated on real production data obtained in the ETA Research Factory.